IEEE Technology and Society Magazine - September 2023 - 103

open online courses (MOOCs) available for students
who want to learn about DCAI on their own
or supplement their formal education. In terms of
academic research, DCAI involves developing new
algorithms, models, and techniques for analyzing
and making predictions based on large data sets
[35]. This research can be applied in a variety of
fields, such as healthcare, finance, marketing, and
social media. Some of the current research topics in
DCAI include explainable AI, which seeks to make
AI models more transparent and interpretable, and
federated learning, which aims to train models in
a decentralized manner while maintaining privacy
and security. Other research areas include reinforcement
learning, transfer learning, etc. [36].
Some challenges of DCAI
Data are often referred to as the new oil, but
refining them into meaningful insights can be a challenging
process. Many companies are still facing difficulties
when it comes to effectively managing and
utilizing their data. Despite investing significant time
and resources, their efforts often yield little results.
The primary challenges are data volume, consistency,
quality, and bias that industries face when
adopting a DC approach to AI. These challenges can
significantly impact the performance and reliability
of an AI model, so it is important to address them
properly [37], [38]. Organizations must ensure that
they have a sufficient volume of high-quality data
that is consistent and representative of the data that
the model will process after deployment. Blindly collecting
as much data as possible can be inefficient
and costly. Therefore, it is important to establish the
kind of data that is needed before acquiring more
data. In addition, organizations must prioritize an
effective system of data annotation to ensure consistency
in the labeling of the data. An AI model
trained on inconsistent data annotation can quickly
become unreliable.
Biases can be introduced into AI systems when
the training data used to develop the models are
themselves biased. If the training data is not diverse
or representative enough, or if it reflects historical
biases and stereotypes, then the resulting AI models
may perpetuate or even amplify these biases. In
addition, biases can also be introduced during the
selection of features or the design of the algorithm
used to develop the AI model. Therefore, it is important
to carefully select and prepare training data and
September 2023
algorithmic models to minimize the potential for
biases in AI systems [39], [40]. For example, in 2018,
Amazon had to abandon its AI-based hiring tool,
which was designed to help automate and streamline
the recruitment process. The tool was trained on
resumes submitted to the company over a 10-year
period, which were predominantly from men as
the tech industry is male-dominated. As a result, the
tool learned to prefer male candidates over female
candidates, reflecting the biases present in the training
data. Amazon's tool was ultimately not used
because it perpetuated the existing gender bias in
the tech industry, rather than mitigating it. This case
highlights the importance of ensuring that training
data is diverse and representative and that AI models
are tested for fairness to avoid perpetuating or amplifying
biases [41], [42].
Few feasible solution approaches
The solution to DCAI challenges requires a multifaceted
approach. Data sources should be diversified
to ensure the algorithm is exposed to a more
representative sample of the population. Algorithms
should be audited regularly to identify biases and
provide opportunities for correction. Ethical considerations
should be included in the development process
[3]. Human oversight can help identify potential
biases and ensure ethical decision-making. Monitoring
and evaluating the algorithm's performance can
help identify and mitigate biases over time, ensuring
reliable and ethical results.
Transparency
Transparency in AI decision-making processes is
crucial for several reasons: When AI systems make
decisions that affect people's lives, it's important to
know how those decisions are being made. Transparency
allows for the identification of biases or errors
in the decision-making process, and it helps ensure
that those responsible for the AI systems can be held
accountable for their actions [3]. For building trust
between users and AI systems, transparency plays
a key role. If users don't know how an AI system is
making decisions, they are unlikely to trust the system.
On the other hand, if the decision-making process
is transparent, users are more likely to trust the
system, which can lead to greater adoption and use.
Transparency can help to ensure that AI systems
are making decisions fairly. By making the
decision-making process transparent, it is easier
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IEEE Technology and Society Magazine - September 2023

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